The table 6.1 gives the mixing probabilities and the associated parametricvalues fork(number of components) = 2,3, and 4. It may be noted thatthe Log likelihood value is smaller fork= 4 (the results fork= 5 , 6 etc.are not better than that fork= 4 and hence are not given here). The fourcomponents Poisson Mixture model is given in table 6.2. It may be notedthat 58% of wards may have higher incidence/relative risk and the remainingwards have lesser/lower incidence for the Cancer disease. We computed theposterior probability for each component for each ward (see table 6.3). Eachward is assigned to a particular component so that the posterior probability islarger. These results are also given in table 6.3 Finally we present Choroplethmaps based on those results

The Posterior Probability of Mixing Dis-tribution

Algorithm

EM Procedure

Poisson Mixture

Data Sources

Poisson Mixtures Distribution

Poisson Model

We have analysed the Cancer data of patients in 155 wards of Chennai Cor-poration by the above described method. As preliminary analyses, we havecreated the Choropleth maps for Observed counts, Population of wards, ex-pected counts for patients and SMR's.The Choropleth map for the observed counts Figure 5.2 does not show anypattern. But the Choropleth map for the expected counts Figure: 5.4 indi-cate that the inner regions of the Chennai Corporations have lower expectedcounts and the regions along the border have larger counts of patients. As ameasure of spatial heterogeneity we have computed PSH= 0:7108:Hence ofthe total spatial random variation, nearly 71% is due to spatial heterogene-ity and the remaining 28:92% is due to Poisson variation. Thus the spatialvariation is present in the data.The Choropleth map for Empirical Bayes smoothed rates Figure 5.5 re-veals that only 13 sub regions have high risk values. The wards with numbers53, 64, 67, 70, 78, 93, 100, 103, 110, 117, 122, 147 and 151 have high riskvalues. Though this information could be used by the health managers toconcentrate their work on these regions, one can look for additional covariatesin these regions for further study

Empirical Bayesian Smoothing

Incidence Rate and SMR

Spatial Analysis of Cancer PatientCount Data

Madurai Kamraj University

Selvi, P Vetri

Statistical spatial modelling and analysis of disease count data

Haemocyte morphogenesis

Spreading inhibitory behavior

Inhibition of haemocyte aggregation

Haemolymph protein profile

Total haemocyte count assay

Immunomodulatory

Helicoverpa armiger

Spodoptera litura

Gut enzyme profile

Insecticidal activity

. Statistical analysis

Inhibition of haemocytes spreading behavior

. Inhibition of haemocytes aggregation behavior

Haemolymph protein profiling

Total haemocyte count (THC)

Haemolymph collection

Immunomodulatory

Lactate dehydrogenase

Asparate (AAT) and Alanine aminotransferase (ALT)

Gut enzyme profile

Oral toxicity bioassay

Microinjection bioassay

VS preparation

Insect collection

INSECTICIDAL AND IMMUNOMODULATORY ACTIVITY AGAINST INSECT PEST

Utilization of VS by the prey

Influence of prey on VS yield

Quantification of protein of VS yielded in the prey deprivation

Survival (SR) and venom milking rate (VMR

Venomous saliva optimization

Statistical analysi

Quantification of VS for protein

Venomous saliva utilization

Prey type

Starvation and collection method

Venomous saliva optimization

Insects Collection

VENOMOUS SALIVA: OPTIMIZATION AND UTILIZATION

Manonmaniam Sundaranar University

Vinoth Kanna, A

Rhinoceros fuscipes fab Venomous saliva biological and immunomodulatory activity against insect pest and mice

Salivary gland-Histology

Salivary gland-Morphology and Anatomy

Maxillary stylet

Mandibular stylet

Head

Gross morphology and histology of salivary gland

Morphometry of head and stylet

Head and stylet preparation

Insect collection and maintenance

GROSS MORPHOLOGY OF THE REDUVIID HEAD AND SALIVARY GLAND COMPLEX

Statistical analysis

Micronuclei test

SDS -PAGE for serum protein analysis

Estimation of serum proteins (SDS –PAGE) and Micronuclei in fish, Labeo rohita

Estimation of bioaccumulation of trace metals inmuscle and gill tissue of fish, Labeo rohita

Lactic dehydrogenase

Malic dehydrogenase

Succinic dehydrogenase

Estimation of dehydrogenases activities (SDH, MDHand LDH) in muscle and gill tissue of fish, Labeo rohita

Alkaline Phosphatase

Acid Phosphatase

Estimation of Phosphatases activities (Acid and Alkaline Phosphatase) in muscle and gill tissue of fish, Labeo rohita

Cholesterol content

Glycogen content

Protein content

Estimation of biomolecules (Protein, glycogen and cholesterol) inmuscle and gill tissue of fish, Labeo rohita

Fish sampled from selected water bodies for further analysis

Characteristic features ofLabeo rohita

Animal model for the study-Labeo rohita

Analysis of Physico-chemical parameters of the water sampled

Survey of lakes to check their status of pollution

Bangalore University

Nazima Noor

Metabolic responses of labeo rohita reared in selected lakes of Bangalore

Increments in biomass:

Residual nutrients

Chlorophyll content in waters:

Growth maximum values

Rates of growth (OD678/day

Growth

Experimental Set up

BIOMASS PRODUCTION: ROLE OF N-P CONCENTRATION AND RATIOS